[CCICADA-announce] DIMACS/CCICADA Interdisciplinary Seminar Series and Rutcor Presents Patrice Perny

[Linda Casals]

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DIMACS/CCICADA Interdisciplinary Seminar Series and Rutcor Presents

Patrice Perny, University Pierre and Marie Curie, Paris, France for a special talk.              

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Patrice Perny, University Pierre and Marie Curie, Paris, France
 will be visiting DIMACS 3/28/11-4/29/11.

Partice's research interests include:

  * decision making under uncertainty and risk

  * multicriteria decision making

  *  multiobjective combinatorial optimization

  *  social choice and group decision making

  *  preference modelling, preference elicitation, graphical models

  *  decision support systems

Patrice will give a special talk (abstract below) sponsored by the
DIMACS/CCICADA Interdisciplinary Seminar Series and Rutcor on 
April 25, 2011 at 3:00 pm.  

There will be a reception in his honor in the
4th floor lounge following the talk.

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Title: Algorithmic Decision Theory and Preference-based Optimization 

Speaker: Patrice Perny, University Pierre and Marie Curie, Paris, France 

Date: Monday, April 25, 2011 3:00 pm

Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University, Busch Campus, Piscataway, NJ 

Sponsored Jointly by Rutcor and the DIMACS/CCICADA Interdisciplinary Seminar Series. 

Abstract: 

The developments of Decision Theory in the last decades have provided
a variety of sophisticated preference models for decision making in
complex environments (uncertainty and risk, multicriteria decision
making, collective decision making). In this area, much effort is
devoted to the axiomatic analysis of preferences and the theoretical
justification of mathematical models, for descriptive or prescriptive
purpose. In most cases, less attention is devoted to the determination
of the preferred solutions, that can easily be deduced from the
model. However, when the set of feasible solutions has a combinatorial
structure and/or is implicitly defined (e.g. by a set of constraints),
the specification of the preference model is not sufficient. The
determination of the preferred solutions requires a computational
effort and raises new algorithmic issues due to the particular
structure of preference models. For example, simple constructive
approaches used in combinatorial optimization (e.g. dynamic
programming, greedy search) do not automatically extend to cope with
non-standard preferences models (e.g. partial order on solutions,
non-linear cost functions). This suggests revisiting standard
optimization problems considered in Operations Research textbooks, in
the light of Decision Theory, and developing new algorithms for
preference-based optimization. The aim of this presentation is to
provide an overview of problems currently investigated along this line
in Algorithmic Decision Theory. We will introduce preference-based
optimization problems in various contexts such as compromise search in
multiobjective optimization, fair optimization for multi-agents
decision making, robust optimization and dynamic decision making under
uncertainty. For some of these problems, we will discuss complexity
issues and propose some solution algorithms.